Build a Search-Powered Personal Assistant App with Anthropic's Claude and LangChain

Introduction

Introducing Anthropic's Claude—an advanced AI assistant designed to be helpful, honest, and harmless. With its versatile capabilities, Claude empowers users in various domains, making it an ideal tool for a range of applications.

Key Features of Claude

• Conversational and text processing abilities
• User safety and privacy as top priorities
• Primary use cases: summarization, search, writing assistance, Q&A, and coding help

Introduction to LangChain

LangChain is a powerful framework for building Language Learning Model (LLM) applications. It simplifies the development of AI models capable of understanding and generating human-like text.

Prerequisites

• Basic knowledge of Python
• Basic knowledge of TypeScript and/or React
• Access to Anthropic's Claude API
• Access to SerpApi's Web Search API

Outline

1. Initializing the Project
2. Building the Front-End for an AI Assistant App with Claude and LangChain
3. Writing the Project Files
4. Testing the AI Assistant App
5. Discussion

Initializing the Project

Setting Up Your Flask Application

To create a simple application, follow these steps:

1. Install Flask:

pip install Flask

2. Create a new directory and navigate to it:

mkdir claude-langchain && cd claude-langchain

3. Set up a virtual environment:

python -m venv venv
source venv/bin/activate (Linux/Mac)
venv\Scripts\activate (Windows)

4. Create app.py file:

touch app.py

5. Add Flask application code:

from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
  return 'Hello, World!'
if __name__ == '__main__':
  app.run()

6. Run the Flask application:

python app.py

7. Open your browser and visit http://127.0.0.1:5000/

Configuring Environment Variables

1. Install python-dotenv and langchain:

pip install python-dotenv langchain

2. Create a .env file and add your API keys:

ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxxxxxxx
SERPAPI_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

3. Load environment variables in app.py:

import os
from flask import Flask
from dotenv import load_dotenv
load_dotenv()
app = Flask(__name__)
...

Building the Front-End for an AI Assistant App

Installing Required Tools

1. Download Node.js and npm from the official site.
2. Install Create React App globally:

npm install -g create-react-app

3. Create a new project with TypeScript:

npx create-react-app ai-assistant-claude --template typescript

Integrating TailwindCSS

1. Install Tailwind CSS:

npm install -D tailwindcss postcss autoprefixer

2. Initialize Tailwind CSS:

npx tailwindcss init -p

3. Configure template paths in tailwind.config.js.
4. Add Tailwind directives to src/index.css.

Finalizing the Setup

Install any additional necessary libraries, such as:

• fontawesome for icons
• react-markdown for Markdown content
• axios for API requests
• react-hook-form for managing form state

Writing the Project Files

We'll expand our app.py to include new endpoints.

Implementing Endpoints

• /ask: For basic chat interactions using Claude.
• /search: For enhanced responses utilizing the SerpAPI.

Testing the Endpoints

Run the application and use a tool like Insomnia to test your APIs:

curl -X POST http://127.0.0.1:5000/ask -H "Content-Type: application/json" -d '{"question":"What about Yakuza?"}'

Testing the AI Assistant App

To test your assistant app, ensure both the backend and frontend are running:

npm start

Use the app to navigate through the features and test the responses from the AI model.

Conclusion

This tutorial demonstrated how to build an AI assistant app leveraging Claude and LangChain. With the right frameworks and libraries, integrating AI responses into a user-friendly interface is efficient and productive.

By combining both the context-aware /ask endpoint and the web-enabled /search endpoint, you create a robust AI assistant that engages users effectively!